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Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network
Yi Zhang1,2,3,4,5, Qin Yang1,5, Lifu Zhang1,5
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
Journal of Neural Engineering
|March 10, 2021
Summary
Noise-assisted multivariate empirical mode decomposition (NA-MEMD) offers a novel causal decomposition method based on phase dependence, not prediction. This approach reveals how causes produce effects, applicable to complex dynamic systems and brain processes.
Area of Science:
- Complex Systems Analysis
- Neuroscience
- Time Series Analysis
Background:
- Existing causality inference methods often rely on prediction or temporal precedence.
- Understanding true causal relationships requires methods that capture underlying mechanisms.
- Philosophical concepts of causality (Hume, Kant) emphasize covariation and power.
Purpose of the Study:
- To introduce and validate a Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based causal decomposition approach.
- To demonstrate that causal relationships are fundamentally phase-dependent, not merely predictive.
- To establish that candidate causes actively produce effects.
Main Methods:
- Development of a NA-MEMD based causal decomposition framework.
- Analysis of time series data focusing on phase relationships and power dynamics.
- Validation against established methods like Granger causality and standard empirical mode decomposition.
Main Results:
- NA-MEMD based causal decomposition identifies causality through phase dependence, distinguishing production from mere succession.
- The method is shown to be applicable to bivariate and multiscale time series.
- Validation confirms its utility in neuroscience, particularly for brain physiological processes.
Conclusions:
- NA-MEMD based causal decomposition provides a robust framework for inferring causality in complex dynamic systems.
- This phase-dependent approach offers deeper insights into cause-effect interactions than predictive models.
- Potential applications include advanced causality inference in various scientific domains.
